GEO BON - a global Biodiversity Observation Network

The Group on Earth Observations or GEO (www.earthobservations.org ) is a partnership of more than 70 nations and 50 participating organisations. GEO is designing a Global Earth Observing System of Systems
(GEOSS) in order to improve the coordination of new and existing Earth observation data sets.

Biodiversity is one of the nine Societal Benefit Areas recognised by GEO. A Biodiversity Observation Network - GEO BON - is one of the first systems that GEO is producing for the GEOSS.

The GEO BON Implementation Plan was released on International Biodiversity Day.

Working Group 1: genetic/phylogenetic diversity

One of the current working groups for GEO BON is interested in global and regional scale monitoring of genetic and phylogenetic diversity (WG1).

The GEO BON Concept Document describes one important strategy for WG1:

"GEO BON will address the integration of remote sensing observations and in situ observations at the genetic level. GEO BON will use approaches that side-step the accumulation of genetic observations over time and instead rely on remotely sensed observations, over time, of changes in land/water condition. Here, spatial genetic variation models act as the “lens” for interpreting these changes. An example context is found in molecular approaches such as those used for microbes that provide patterns of genetic variation summarized as phylogenetic patterns. These patterns of genetic variation may be quantified using a measure of phylogenetic diversity or dissimilarity. The links from phylogenetic dissimilarities to environmental variables provide a basis for developing spatial models of genetic variation, as in the striking finding that global bacteria genetic diversity patterns (using 16S ribosomal RNA sequences) are strongly linked to salinity factors (Lozupone et al 2007). GEO BON will promote the use of such spatial biodiversity models as a “lens” to interpret the observed changes over time in ecosystem and habitat metrics (such as the presence or absence of particular ecosystems, their surface area, and degree of fragmentation) derived from remote sensing. GEO BON will explore the extension and standardization of these strategies."

The analysis of microbial 16s data has used phylogenetic dissimilarities and ordination models to compare samples and reveal underlying environmental factors or gradients. However, there are challenges in refining these methods for the “lens” approach in GEO BON. Some of these issues are addressed in:

That paper points to robust ordination methods, developed elsewhere in ecology, that can overcome some of the current perceived difficulties in modelling 16s data in microbial ecology. For example, the GEO BON lens approach requires robust ordinations that avoid distortions such as the well-known arch or horseshoe problem. In considering a wide range of dissimilarity measures and ordination approaches, microbial workers have run head-long into this problem.

Kuczynski et al (2010) argue
“The arch effect (where samples along a single environmental gradient are misleadingly placed in an arch configuration) was prominent in the simulated data, where we know there is only a single gradient. The presence of the same effect in the soil data suggests that the pH gradient in the soil was the single driving factor in these communities”

More recently, Gonzalez and Knight (2012; Advancing analytical algorithms and pipelines for billions of microbial sequences. Current Opinion in Biotechnology 23:64–71) say:
“Another approach that can reduce certain artifacts, such as the horseshoe effect (a pattern in which the two ends of an axis attract each other due to a shared lack of the taxa in the middle, thus obscuring the gradient pattern), is to use nonlinear methods. NMDS can better preserve the highdimensional structure with few axes in some cases, although cannot fully avoid the arch effect in realistic microbial datasets. The main differences between PCoA and NMDS are that the former is based on distances, where the final configuration should match the original distances as close as possible, and the latter is based on ranks..”

The Faith et al (2009) paper referred to earlier work based on Bray Curtis type dissimilarities and hybrid multidimensional scaling (HMDS or SSH) that overcomes the arch problem and provides a robust ordination space suitable for inferring biodiversity loss through the lens approach (and ED methods).

Identifying the robust framework for ordination of 16s data addresses one challenge, but still leaves open the challenge of practical ordination algorithms for large data sets. WG1 workers are exploring approaches that are based on the robustness of Bray Curtis type dissimilarities and hybrid multidimensional scaling - but side-step the computationally-intensive ordination and work directly with the dissimilarities in ED methods. The rationale is that, for applications of the lens approach, we do not have to look at the ordination and so can use shortcuts to calculate ED biodiversity loss scores.

These strategies may assist GEO BON and programs such as the Earth Microbiome Project.

Also, an invited presentation at: "Strategies in taxonomy: research in a changing world" (20-22 May 2009, Pruhonice, Czech Republic). My talk addressed one GEO BON candidate strategy: "Estimation of status of biodiversity for different places at different times changes in land/water condition (e.g. using remote sensing), integrated with spatial genetic/phylogenetic variation models as the “lens” to infer corresponding changes at the genetic/phylogenetic levels."

The described approach uses PD-dissimilarities* among samples to create models linking phylogenetic spatial variation to environmental and other gradients. We then apply standard species-level methods to create indices reflecting phylogenetic diversity losses from climate and land use change. These methods are based on the ED (environmental diversity) method, which "counts-up” species under an evolutionary model of unimodal response of features to gradients. My conference presentation concluded that these methods may serve GEO BON, by integrating phylogenetic information into a “lens” for interpreting remotely sensed changes in land condition.